LambdaRankIC derives closed-form lambda gradients for pairwise rank swaps to directly optimize Rank IC within the LambdaRank framework, outperforming regression and NDCG losses on simulated and real financial data.
Management Science , volume=
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LLM filtering of embedding-based stock networks raises long-short Sharpe ratio from 0.742 to 0.820 and cuts max drawdown from -10.47% to -7.85% in 2011-2019 S&P 500 backtests.
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LambdaRankIC: Directly Optimizing Rank IC for Financial Prediction
LambdaRankIC derives closed-form lambda gradients for pairwise rank swaps to directly optimize Rank IC within the LambdaRank framework, outperforming regression and NDCG losses on simulated and real financial data.
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Cross-Stock Predictability via LLM-Augmented Semantic Networks
LLM filtering of embedding-based stock networks raises long-short Sharpe ratio from 0.742 to 0.820 and cuts max drawdown from -10.47% to -7.85% in 2011-2019 S&P 500 backtests.